Mining Information for Instance Unification

Aswani, Niraj; Bontcheva, Kalina; Cunningham, Hamish

doi:10.1007/11926078_24

Cited by 28 publications

(19 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The system by Aswani et al [1] needs to interact with search engines to retrieve context information and thus may not scale to large datasets. RiMOM [23] and Silk [20] rely on human provided matching rules and thus costly to customize to new domains.…”

Section: Related Workmentioning

confidence: 99%

“…Considering the scale of Linked Data, approaches that perform a brute-force comparison on every pair of instances [1,16] are less likely to succeed. As a key part of this proposal, we will explore novel approaches to scaling entity coreference on large datasets: Candidate selection (CS ) and context pruning (CP ), i.e., doing fewer comparisons vs. doing faster comparisons.…”

Section: Introduction Challenges and Expected Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Song

2012

The Semantic Web – ISWC 2012

View full text Add to dashboard Cite

Abstract. Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources and assigned syntactically distinct identifiers. In order to facilitate data utilization in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process can also be referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. This proposal will investigate algorithms to solve this entity coreference problem in the Semantic Web in several aspects. The essence of entity coreference is to compute the similarity of instance pairs. Given the diversity of domains of existing datasets, it is important that an entity coreference algorithm be able to achieve good precision and recall across domains represented in various ways. Furthermore, in order to scale to large datasets, an algorithm should be able to intelligently select what information to utilize for comparison and determine whether to compare a pair of instances to reduce the overall complexity. Finally, appropriate evaluation strategies need to be chosen to verify the effectiveness of the algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introduction Challenges and Expected Contributionsmentioning

confidence: 99%

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Song

2012

The Semantic Web – ISWC 2012

View full text Add to dashboard Cite

show abstract

“…Similar to our approach to instance merging is [3] where the problem of instance unification for author names is addresses. They mine information from the Web for authors including full name, personal page, and co-citation information to compute the similarity between two person names.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Information Extraction for Business Intelligence

et al. 2007

Self Cite

View full text Add to dashboard Cite

Abstract. Business Intelligence (BI) requires the acquisition and aggregation of key pieces of knowledge from multiple sources in order to provide valuable information to customers or feed statistical BI models and tools. The massive amount of information available to business analysts makes information extraction and other natural language processing tools key enablers for the acquisition and use of that semantic information. We describe the application of ontology-based extraction and merging in the context of a practical e-business application for the EU MUSING Project where the goal is to gather international company intelligence and country/region information. The results of our experiments so far are very promising and we are now in the process of building a complete end-to-end solution.

show abstract

“…This large volume of data requires automatic approaches be adopted for detecting owl:sameAs links. Prior research to this entity coreference problem 2 [1,10,15] has focused on how to precisely and comprehensively detect coreferent instances and good results were achieved. However, one common problem with previous algorithms is that they were only applied to a small number of instances because they exhaustively compare every pair of instances in a given dataset.…”

Section: Introductionmentioning

confidence: 99%

Automatically Generating Data Linkages Using a Domain-Independent Candidate Selection Approach

Song

Heflin

2011

The Semantic Web – ISWC 2011

View full text Add to dashboard Cite

Abstract. One challenge for Linked Data is scalably establishing highquality owl:sameAs links between instances (e.g., people, geographical locations, publications, etc.) in different data sources. Traditional approaches to this entity coreference problem do not scale because they exhaustively compare every pair of instances. In this paper, we propose a candidate selection algorithm for pruning the search space for entity coreference. We select candidate instance pairs by computing a character-level similarity on discriminating literal values that are chosen using domain-independent unsupervised learning. We index the instances on the chosen predicates' literal values to efficiently look up similar instances. We evaluate our approach on two RDF and three structured datasets. We show that the traditional metrics don't always accurately reflect the relative benefits of candidate selection, and propose additional metrics. We show that our algorithm frequently outperforms alternatives and is able to process 1 million instances in under one hour on a single Sun Workstation. Furthermore, on the RDF datasets, we show that the entire entity coreference process scales well by applying our technique. Surprisingly, this high recall, low precision filtering mechanism frequently leads to higher F-scores in the overall system.

show abstract

Mining Information for Instance Unification

Cited by 28 publications

References 4 publications

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Ontology-Based Information Extraction for Business Intelligence

Automatically Generating Data Linkages Using a Domain-Independent Candidate Selection Approach

Contact Info

Product

Resources

About